ToMeeto

Inspiration

College can be lonely. Especially when you're awkward and lonely. We were inspired by multiple posts on UC Berkeley’s Free & For Sale page in which students sought friendly peers to eat with; we decided to streamline this process and make it easy for college students to find meal buddies. With Tomeeto, you can always meet to eat!

What it does

After entering in simple but meaningful information like their majors, hobbies, and emails, students are paired based on current availability. Students are able to see the basic information of their “match,” and are encouraged to contact each other and meet up.

How we built it

We used HTML/CSS for the front-end, Flask for the backend, and Google Cloud Data Store for our database.

Challenges we ran into

Setting up Flask led to hours of furious Googling, and coding the matching process required querying frameworks we’d never used before, with fairly little documentation. Datastore also isn’t great at storing images, so we ran into a roadblock where we attempted to encode our images into base64 and then decode them back into images.

Accomplishments that we're proud of

We’re proud we’ve built a product that we believe could genuinely improve the mental health of our student body. Having once been lonely freshmen, we know the simple act of offering to eat with someone can make their day.

We’re also proud we were able to store images, and that we built a minimum viable product that actually works!

What we learned

We learned how to use Flask and integrate it with Google Cloud Datastore and Google Cloud Storage. We also learned the importance of coming up with user stories to guide our design, and focusing on a minimum viable product.

What's next for Tomeeto

Build accounts for users to save more permanent data, like dietary restrictions, who’ve they’ve matched with before, and whether or not other users have reported them.

Send out emails to the two people within a match, and book an event for them on Google calendar.

Suggest restaurants to pairs based on their location and dietary preferences.